
BPS Cognitive Section Conference, Sept 2022
Dr. Christopher J. Wilson
Centre for Applied Psychological Science, Teesside University
Financial distress detrimental to cognition (Mani et al., 2020; Vohs, 2013)
This can affect cognitive processes such as planning, reasoning and decision-making (de Bruijn & Antonides, 2022; Hinson et al., 2003; Hofmann et al., 2012; Mani et al., 2013; Roby & Scott, 2022).
What’s happening?
Self-control is a limited cognitive resource and exhausting that resource affects subsequent behaviour (“ego depletion” : Baumeister, 2014; Baumeister et al., 1998; Baumeister et al., 2006, 2008; Baumeister & Vohs, 2018)
Self-control depletion might affect financial or risk-based decisions (Fischer et al., 2012; Gerhardt, 2017; Koppel et al., 2019 )
In the lab, a range of tasks have been used to elicit the effect, including those that target affect, attentional control, thought suppression or response inhibition. It has been replicated across labs (Hagger et al., 2016; Hagger et al., 2010)
Debate about whether this is self-control, or general cognitive fatigue. (Hagger et al., 2010; Inzlicht et al., 2014)
In the current research, inhibitory control tasks are also used (Stroop, Go-noGo)
For the purposes of this research, we will refer use the term Cognitive Exertion
Construal theory (Trope & Liberman, 2003):
Construal is both a cause and consequence of cognitive exertion effect (Bruyneel & Dewitte, 2012; Khenfer et al., 2017; Raue et al., 2015; Wan & Agrawal, 2011)
Construal might affect financial or risk-based decisions (Schmeichel et al., 2011; Ülkümen & Cheema, 2011)
Financial literacy has been shown to increase knowledge and change intentions - but only sometimes examines behaviours (Amagir et al., 2018; Kaiser & Menkhoff, 2020; Mandell & Klein, 2009)
The evidence on efficacy is mixed (Lührmann et al., 2015) and appears to be dependent on many contextual factors (Alessie et al., 2013; Allgood & Walstad, 2016; Chardon et al., 2016; Chen et al., 2018; Foster et al., 2015; Henager & Cude, 2017; Meier & Sprenger, 2013)
Could construal play a role - how information is construed affect subsequent behaviour?
There is research suggesting construal might moderate cognitive exertion effects (Krastev et al., 2020).
However:
Research Questions
Study 1: Does construal affect financial decision-making?
Study 2: Are there any neurological indicators that distinguish high- and low-construal?
(Adapted from Brevers et al., 2018)
Expected value of trials: 0, 2.5, 5, 7.5, 10
IV: construal level (High-Construal, Low-Construal and Control condition).
N = 75
| Condition | n |
|---|---|
| Control Condition | 26 |
| High Construal | 25 |
| Low Construal | 24 |

Participants decisions on the coin-toss task were incentivised using a random lottery, where they could win money, based on their coin-toss decisions
“Your choices in this task do matter”
The model was a significantly better fit than the null model (\(χ^2(4) = 441.47, p < 0.01)\) , Pseudo \(R^2\) (fixed effects) = 0.30
A significant likelihood of not gambling when expected value of the coin-toss was 0 and significant likelihood of gambling in trials with higher expected values than 0 (with the exception of the Expected Value at 2.5)
Model 2 was a significantly better fit than Model 1 \((χ^2(2) = 10.60, p < 0.01)\) , \(\delta\)AIC = -6.6, Pseudo \(R^2\) (fixed effects) = 0.32
Examination of the coefficients showed that both High Construal (β = -0.97, p < 0.01) and Low Construal Conditions (β = -0.49, p < 0.05) predicted likelihood of gambling.
Pairwise comparison of the groups showed that High Construal was significantly different to the Control Condition
There is an effect of construal - High Construal associated with lowest probability of “gambling” and Control condition the highest
Changes to Study 2:
Do the behavioural results replicate from Study 1?
Are there different patterns of neurological activation associated with high- and low-construal conditions?


Markers sent via serial/usb to fNIRS for specified events
Levels of hbO and hbR are calculated during certain time periods relative to baseline
Current N = 13
No inferential analysis run yet
We can observe fNIRS patterns of activation
Data analysed using a custom R script (will be available on Github)
Preprocessing to remove noise, movement artifacts etc:
.1 Hz Lowpass filter (heartrate, respiration)
Moving average filter (1.5s)
Linear detrending